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Application of Bayesian Modeling to Management Information Systems: A Latent Scores Approach

Application of Bayesian Modeling to Management Information Systems: A Latent Scores Approach

Sumeet Gupta, Hee-Wong Kim
Copyright: © 2007 |Pages: 24
ISBN13: 9781599041414|ISBN10: 1599041413|EISBN13: 9781599041438
DOI: 10.4018/978-1-59904-141-4.ch006
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MLA

Gupta, Sumeet, and Hee-Wong Kim. "Application of Bayesian Modeling to Management Information Systems: A Latent Scores Approach." Bayesian Network Technologies: Applications and Graphical Models, edited by Ankush Mittal and Ashraf Kassim, IGI Global, 2007, pp. 103-126. https://doi.org/10.4018/978-1-59904-141-4.ch006

APA

Gupta, S. & Kim, H. (2007). Application of Bayesian Modeling to Management Information Systems: A Latent Scores Approach. In A. Mittal & A. Kassim (Eds.), Bayesian Network Technologies: Applications and Graphical Models (pp. 103-126). IGI Global. https://doi.org/10.4018/978-1-59904-141-4.ch006

Chicago

Gupta, Sumeet, and Hee-Wong Kim. "Application of Bayesian Modeling to Management Information Systems: A Latent Scores Approach." In Bayesian Network Technologies: Applications and Graphical Models, edited by Ankush Mittal and Ashraf Kassim, 103-126. Hershey, PA: IGI Global, 2007. https://doi.org/10.4018/978-1-59904-141-4.ch006

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Abstract

This chapter deals with the application of Bayesian modeling as a management decision support tool for management information systems (MIS) managers. MIS managers have to deal with problems which require prediction and diagnosis for decision making. Lacking a proper tool for making informed decisions, MIS managers feel hard-pressed for a scenario analysis which can take into account the proper causal relationships existing in the real world. Bayesian modeling could be an appropriate support tool for such decision making. However, its application to decision support in MIS is different from application to other fields, as the variables in field of MIS are hypothetical. This brings in a need for Bayesian modeling at a hypothetical variable level rather than at the observed variable level. In this chapter we will study how Bayesian modeling can be used as a tool for managerial decision support in MIS. The conclusions of this chapter can also be extended to other social science researches where the variables are hypothetical in nature.

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